Large (>100cm2) cranial defects require well-fitting prosthetic implants to protect the patient's brain from trauma or infection, and to restore an aesthetic shape to the patient's head. During the cranial reconstructive surgery referred to as cranioplasty, if sufficient cranial bone is not available for engraftment, then malleable and biocompatible materials are used to fashion an implant. The manual skill required of the surgeon, risk of the surgical procedure, cost to the patient, and length of the surgery all increase in proportion to the defect's size. Recently, high-resolution 3D computerized tomography (CT) has been shown useful as a basis for design and pre-operative fabrication of a cranial prosthetic, however design and production of a large, well-fitting implant is not a trivial task.
Traditional computer aided design (CAD) software is based on the use of constructive solid geometry (CSG) modeling. The CSG modeling paradigm formalizes the idea of combining a set of ideal geometric shapes, referred to as primitive solids, via regularized Boolean set operations (i.e., U*, ∩*, and -*) to fashion a desired shape. From this perspective, a CSG model is a tree having primitive solids as its leaves (i.e., leaves are nodes without child nodes) and regularized Boolean operations and geometric transformations as nodes of this tree. An example of a CSG tree representing a L-bracket object is shown in FIGS. 1A and 1B [Hoff 89] wherein: portion (a) is a dimensional specification of an L-bracket and portion (b) is the CSG tree representing a combination of three primitives (e.g., two boxes and a cylinder) via regularized Boolean operations that results in the L-bracket model.
These solids can be visually represented with predefined sets of vertices, edges, and polygons, referred to as a boundary representation (B-rep). By combining primitive solids such as cubes, spheres, or tori, a designer can gradually generate new models according to dimensional specifications. However, evaluating B-reps from a CSG solid without redundancy, and retaining topological validity of B-rep solids, usually requires computationally expensive calculation. CAD tools taking a CSG approach eventually attempt to mimic the designer's hand skills and sculpting tools, allowing drawings on canvas via combinations of CSG primitive solids on screen.
Other than a B-rep polygonal mesh, surfaces can also be represented parametrically as: Bezier, B-splines, and non uniform rational B-spline (NURBS) [FvFH 90, Watt 93]. Parametric surfaces require definitions with control and knot points, where each piece-wise curve segment passes through or affects the resulting shape. Sweeping operations on a parametric curve can define a parametric surface, however the tensor product of two parametric surfaces provides more control over the shape of freeform surfaces. Unlike 2D curve drawing tools, the direct manipulation of 3D parametric surfaces via control points is neither intuitive nor conducive to creating a complex freeform surface design. One alternative to direct manipulation is B-spline surface control point schemes derived from previously collected polygonal B-rep models. The conversion requires computationally expensive calculations to solve simultaneous linear boundary point and interior point equations [BaGr 80], which limit the possible degree of the surface complexity and accuracy. However, B-spline surfaces can be flexibly created using techniques such as hierarchical refining of B-spline patches [FoBa 88], and by automated conversion from arbitrary topological types [EcHo 96]. Thus, the traditional CAD approach can be considered as a bottom-up approach, starting with a set of primitives that are split or merged into more complex shapes at the root of a CSG tree [FIG. 1B].
Use of CAD/CAM in Medicine
Producing patient-specific CAD data for computer aided manufacture (CAM) implant production via rapid prototyping (RP) technology has extended the CAD/CAM area to medical imaging applications. The jaggy ‘stair-case’ artifacts seen in RP fabricated models previously due to low resolution-layered fabrication have been resolved by improved device resolution and various surface smoothing and refinement techniques [RaLu 96]. However, volumetric medical image data cannot be input directly into traditional CAD programs (i.e., software), programs that commonly require B-rep format objects. To convert a stack of CT volume data to a B-rep polygonal mesh surface, isosurface construction algorithms, such as Marching Cubes [LoCl 87] or the Wrapper [GuDe 94, GuHu 95] may be employed. The resulting B-rep is referred to as a 3D iso-intensity or isosurface image, where the surface corresponds to the boundary between structures with distinct radio-densities.
Recently, NURBS-based volume modeling was proposed as a means to represent not only the surface boundary but also the interior of medical CAD objects [MaLC 01a]. NURBS volume representations have an operational advantage over B-rep surfaces in terms of simplified Boolean CSG operations that avoid complex boundary problems. Nonetheless, NURBS volume data also require isosurface construction for RP printing since the de-facto standard file format for most current RP CAM (Computer Aided Manufacturing) is the stereolithography (STL) format, which describes a B-rep [MaLC 01b].
Most medical CAD research has been focused on the CAD/CAM of artificial hip and knee prosthetic implants. Recently, patient-specific hip implants have been an option added to the off-the-shelf implant designs produced by the traditional CAD approach. Patient-specific morphology includes hip implant stems that better fit when they are inserted into the bone marrow cavity of a patient's femur. Adams et al. [AHPK 02] suggest designing these patient-specific implants on the basis of femoral 3D CT scans with 2-5mm slice thickness. The resulting implant designs are then verified in a virtual implantation system evaluating anatomical parameters such as the amount of patient bone removal and femoral attachment contact surface location. Also, a hybrid CAD environment that supports direct parametric surface editing of high resolution CT volume images have been suggested by Viceconti et al. [VTGZ 01].
Use of CAD/CAM for Cranial Implants
Eufinger et al. [EuSa 01] claim that craniofacial structures require higher accuracy, therefore requiring use of larger image data sets than the femoral bone CT data utilized for implant design. They also note that interactive CAD processing of the requisite large image data files was not computationally possible until the 1990s. Cranial prosthetic implants must accurately fit the defect site in order to reduce the possibility of subsequent movement, dislodging, or extrusion [MaVa 90]. For this reason, early papers on patient-specific craniofacial implant fabrication suggest the use of a negative impression directly from the patient's exposed skull with vulcanizing silicon [MoPF 76].
The use of skull models obtained by RP from 3D CT data as a guide to manual implant production is computationally less expensive than a full CAD approach to implant design and fabrication. Several surgical supply companies have recently begun to produce solid plastic models of the patient's anatomy via RP techniques, such as stereolithography [SmBu 01, VoSA 20], based on 3D CT data. Modern RP systems produce models with accuracy up to 0.05 mm resolution1, which exceeds the accuracy of conventional clinical CT data acquisition. A cranial prosthetic implant is then produced by manually molding plastic material on the surface of the 3D printed skull model. A negative impression (mold) is made of this hand-modelled implant, and this mold is then used to make a cast in implantable material. This method is known to produce better-fitting prosthetic implants than traditional CAD/CAM prefabrication methods. A clinical trial using this technique has reported 93.1% excellent intra-operative implant fit among 29 patients [SaHK 02]. These implants were cast in biocompatible materials such as CFRP (Carbon Fibre Reinforced Polymer) [SaHK 02], Polymethylmetacrylate (PMMA), or Proplast II [VoSA 20]. Although this method makes full use of computer algorithms to visualize and produce a 3D skull model, it is not, strictly, a CAD approach since the implant is manually sculpted by a skilled technician. RP produced skull models also have been found to be useful for preoperative diagnosis and surgical planning [SHZW 98]. 1Viper si2 SLA system specification, 3D Systems©, Valencia Calif.
Irrespective of the accuracy, manually producing a cranial implant is an expensive process due to the cost of fabricating RP skull models and the time a board-certified anaplast or a prosthodont must bill to produce the implant. Usually the CAM systems are not in-house resulting in further delay [SmBu 01], and these implants cannot be exactly reproduced [WEKM 95]. Moreover, the absence of soft-tissue information removed during the skull segmentation process leading to an RP skull model raise risks of the improper fit of the implant due to unanticipated intersection with over- and under-lying soft-tissue layers, the scalp and dura mater, respectively [Bond 02].
An attempt to replace the manual design of an implant for unilateral cranial defect for one designed entirely on computer (i.e., Computer Aided Design, CAD) by reflecting the normal side of the cranium onto the defect side has been suggested by Linney and colleagues [LTRG 93]. Unfortunately, this technique cannot be applied to cases where the defect crosses over the mid-sagittal plane (i.e., a bilateral cranial defect) of the cranium. A concrete and practical CAD approach by Wehmöller et al. [WEKW 95] allows an operator to draw space curves and freeform surfaces with traditional CAD tools on the patient's skull image that is rendered as a stack of contours. The output data is then sent to a CNC milling machine to produce a solid titanium implant. Although their method may produce a reasonably fitting implant, the continuity of curvature across the surface at the implant-patient contact may be insufficient. This is because the resulting implant surface shape is derived solely from an operator's hand drawings that do not ensure a sufficient between-slice articulation of the contours. Carr et al. [CaFB 97] report a novel approach to cranial implant surface design using a surface interpolation method based on the TPS (Thin Plate Spline) radial basis function. Their work presents use of the TPS interpolation to deform an ideally thin planar surface to the defect site. Due to the high computational expense of the TPS interpolation method, they deferred use of this method until faster evaluation methods and increased computational capacity was-available. Their cranial implant shows an excellent continuity of surface curvature across the defect margin, benefiting from the property of the TPS interpolation.
Most CAD/CAM approaches to patient-specific cranial prosthetic implants have used titanium as the implant material for CNC milling machines [WEKW 95, CaFB 97, HFBL 98, JNRL 99, EuSa 01, KWSW 01]. The thickness specification of the titanium implants is usually chosen as 1.5 mm or less [EuSa 01]. This thin specification facilitates flexibility for designing the implant rim description, often with screw holes that partially overlap with cranial defect margin [JNRL 99]. Although this simplifies the CAD steps, the CAM phase of titanium processing can raise difficulties due to machining strength limits of many RP systems [WEKW 95]. A survey evaluating post-operative performance of CAD/CAM fabricated titanium cranial implants reports 76 out of 78 patients having successful outcomes [KWSW 01].
The primary problem preventing modeling of biomedical shapes using traditional CAD/CAM is the size of the original CT volume image and derived polygonal mesh image data sets. Most of current cranial implant CAD approaches need to partition the defect site during the design phase if the defect is as large [WEKW 95, CaFB 97] as those analyzed in this patent application. However, using anatomical models as primitives in a bottom-up CAD approach would require extremely expensive calculations for operations such as locating boundary intersections between hundreds of thousands of polygons. The traditional CAD approach of combining mathematical primitive models to design an implant may improve interactivity, but it also involves a significant amount of the designer's time for manual editing. Moreover, it may fail in maintaining fidelity to the inevitably complex patient-specific defect region dimensions. Thus, accurate 3D isosurface models derived from patient 3D-CT scans of the defect site should be utilized at the initial stage of the design process to guarantee sufficient implant accuracy (i.e., good fit) and performance (i.e., subsequent protection from trauma and infection).